IXXI network/signal seminar: "Distance, weight and laminar relations in the mammalian cortex"
Dr. Kenneth Knoblauch (Cortical Architecture, Coding and Perception Team, Stem Cell and Brain Research Institute, Inserm UCB, Lyon)
de 10:00 à 11:00
|ENS de Lyon - Monod campus - room R115 (1st floor).
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The mammalian brain is a complex network of neurons and neural circuits that perform computations permitting the organism to interpret and interact with its environment in order to survive and reproduce over its lifetime. The human cerebral cortex, the supposed seat of the mammalian brain’s higher functions, contains billions of neurons that communicate through trillions of synaptic connections. At a more molar level, one finds functional columns of neurons and laminar patterns of connectivity. Finally, the cortex can be parcelled into areas that appear to modularize certain functions. Describing the organization of this network is a critical aspect of understanding how it functions. Graph theory is a natural formalism within which to approach this complex network with neurons as the edges and their synaptic connections as the nodes. This is a difficult level to access directly and most descriptions analyze more molar levels of organization. We have used retrograde tract tracing in macaque and rodent models to analyze inter-areal and laminar relations. We have generated a unique directed and weighted data base of the inputs to a sample of areas distributed across the cortex. In contrast to previous studies that considered connectivity on a purely topological basic, our results reveal the cortex as a dense, spatially embedded network, in which specificity is determined by the weight distributions among areas. We have shown that a simple distance/weight law is sufficient to account for a surprising number of features of the data in both species, including wire minimization and a dense core/periphery structure, suggesting a universal principle of organization. In addition, the laminar relations define an order relation between areas that permits us to estimate a directed hierarchy between areas that has recently been shown to be reflected in functional measures indicating signatures of feed-forward and feedback interactions between areas.